Voyage AI Rerank 2.5 Lite

Latency-optimized version maintaining instruction-following and 32K context capabilities with streamlined inference. Designed for high-volume production deployments prioritizing cost efficiency over maximum accuracy. If you want to compare the best rerankers for your data, try Agentset.

Leaderboard Rank

#6 of 12

ELO Rating

1520

#6

Win Rate

53.4%

#6

Accuracy (nDCG@10)

0.103

#4

Latency

616ms

#8

Model Information

  • Provider: Voyage AI
  • License: Proprietary
  • Price per 1M tokens: $0.020
  • Release Date: 2025-08-11
  • Model Name: voyage-rerank-2.5-lite
  • Total Evaluations: 3300

Performance Record

  • Wins: 1763 (53.4%)
  • Losses: 1416 (42.9%)
  • Ties: 121 (3.7%)

Rerankers Are Just One Piece of RAG

Agentset gives you a managed RAG pipeline with the top-ranked models and best practices baked in. No infrastructure to maintain, no reranking to configure.

Performance Overview

ELO ratings by dataset

Voyage AI Rerank 2.5 Lite's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.

Voyage AI Rerank 2.5 Lite - ELO by Dataset

14291479152915791614arguanabusiness reportsFiQaMSMARCODBPediaPG

Detailed Metrics

Dataset breakdown

Performance metrics across different benchmark datasets, including accuracy and latency percentiles.

arguana

  • ELO: 161463.6%
  • WR: 350W-199L-1T
Accuracy Metrics
  • nDCG@50: 0.436
  • nDCG@100: 0.496
  • Recall@50: 0.800
  • Recall@100: 0.980
Latency Distribution
  • Mean: 636ms
  • P50 (Median): 613ms
  • P90: 819ms

business reports

  • ELO: 160054.7%
  • WR: 301W-230L-19T
Accuracy Metrics
  • nDCG@50: 0.000
  • nDCG@100: 0.000
  • Recall@50: 0.000
  • Recall@100: 0.000
Latency Distribution
  • Mean: 599ms
  • P50 (Median): 611ms
  • P90: 727ms

FiQa

  • ELO: 153752.4%
  • WR: 288W-253L-9T
Accuracy Metrics
  • nDCG@50: 0.111
  • nDCG@100: 0.122
  • Recall@50: 0.103
  • Recall@100: 0.135
Latency Distribution
  • Mean: 639ms
  • P50 (Median): 613ms
  • P90: 819ms

MSMARCO

  • ELO: 147647.8%
  • WR: 263W-238L-49T
Accuracy Metrics
  • nDCG@50: 0.000
  • nDCG@100: 0.000
  • Recall@50: 0.000
  • Recall@100: 0.000
Latency Distribution
  • Mean: 563ms
  • P50 (Median): 610ms
  • P90: 619ms

DBPedia

  • ELO: 145948.0%
  • WR: 264W-243L-43T
Accuracy Metrics
  • nDCG@50: 0.000
  • nDCG@100: 0.000
  • Recall@50: 0.000
  • Recall@100: 0.000
Latency Distribution
  • Mean: 587ms
  • P50 (Median): 612ms
  • P90: 656ms

PG

  • ELO: 143454.0%
  • WR: 297W-253L-0T
Accuracy Metrics
  • nDCG@50: 0.000
  • nDCG@100: 0.000
  • Recall@50: 0.000
  • Recall@100: 0.000
Latency Distribution
  • Mean: 670ms
  • P50 (Median): 615ms
  • P90: 818ms

Build RAG in Minutes, Not Months

Agentset gives you a complete RAG API with top-ranked rerankers and embedding models built in. Upload your data, call the API, and get accurate results from day one.

Code Examples

import { Agentset } from "agentset";

const agentset = new Agentset();
const ns = agentset.namespace("ns_1234");

const results = await ns.search(
  "What is multi-head attention?"
);

for (const result of results) {
  console.log(result.text);
}

Compare Models

See how it stacks up

Compare Voyage AI Rerank 2.5 Lite with other top rerankers to understand the differences in performance, accuracy, and latency.